Proceedings Paper

Gerchberg–Saxton-type (GS-type) algorithms have been widely applied in photonics to reconstruct the object structures.
However, using random guesses as the initial inputs, the reconstruction quality of GS-type algorithms is unpredictable.
And, it always leads to a large number of iterations to reach convergence. In this paper, a singular value decomposition
(SVD) based method is proposed to generate an effective phase guess for GS-type algorithms using a low rank
approximation. Experimental results demonstrate that under the same reconstruction error, the proposed SVD based
guesses reduce the iteration times by more than 50% on average compared with that of random guesses. Furthermore,
they can outperform random guesses both in terms of steady state error and iteration times. Compared with the average
performance of random guesses, the proposed approach reduces the steady state error of recovered images by 70.7% on
average and reduces the iteration times by 56.1% on average.